Motivation The principal lines of research in MS/MS based Proteomics have been directed toward the molecular characterization of the proteins including their biological functions and their implications in human diseases. Recent advances in this field have also allowed the first attempts to apply these techniques to the clinical practice. Nowadays, the main progress in Computational Proteomics is based on the integration of genomic, transcriptomic and proteomic experimental data, what is known as Proteogenomics. This methodology is being especially useful for the discovery of new clinical biomarkers, small open reading frames and microproteins, although their validation is still challenging. Results We detected novel peptides following a proteogenomic workflow based on the MiTranscriptome human assembly and shotgun experiments. The annotation approach generated three custom databases with the corresponding peptides of known and novel transcripts of both protein coding genes and non-coding genes. In addition, we used a peptide detectability filter to improve the computational performance of the proteomic searches, the statistical analysis and the robustness of the results. These innovative additional filters are specially relevant when noisy next generation sequencing experiments are used to generate the databases. This resource, MiTPeptideDB, was validated using 43 cell lines for which RNA-Seq experiments and shotgun experiments were available. Availability and implementation MiTPeptideDB is available at http://bit.ly/MiTPeptideDB. Supplementary information Supplementary data are available at Bioinformatics online.

Monocytes are bone marrow-derived leukocytes that are part of the innate immune system. Monocytes are divided into three subsets: classical, intermediate and non-classical, which can be differentiated by their expression of some surface antigens, mainly CD14 and CD16. These cells are key players in the inflammation process underlying the mechanism of many diseases. Thus, the molecular characterization of these cells may provide very useful information for understanding their biology in health and disease. We performed a multicentric proteomic study with pure classical and non-classical populations derived from 12 healthy donors. The robust workflow used provided reproducible results among the five participating laboratories. Over 5000 proteins were identified, and about half of them were quantified using a spectral counting approach. The results represent the protein abundance catalogue of pure classical and enriched non-classical blood peripheral monocytes, and could serve as a reference dataset of the healthy population. The functional analysis of the differences between cell subsets supports the consensus roles assigned to human monocytes.

A comprehensive study of the molecular active landscape of human cells can be undertaken to integrate two different but complementary perspectives: transcriptomics, and proteomics. After the genome era, proteomics has emerged as a powerful tool to simultaneously identify and characterize the compendium of thousands of different proteins active in a cell. Thus, the Chromosome-centric Human Proteome Project (C-HPP) is promoting a full characterization of the human proteome combining high-throughput proteomics with the data derived from genome-wide expression profiling of protein-coding genes. Here we present a full proteomic profiling of a human lymphoma B-cell line (Ramos) performed using a nanoUPLC-LTQ-Orbitrap Velos proteomic platform, combined to an in-depth transcriptomic profiling of the same cell type. Data are available via ProteomeXchange with identifier PXD001933. Integration of the proteomic and transcriptomic data sets revealed a 94% overlap in the proteins identified by both -omics approaches. Moreover, functional enrichment analysis of the proteomic profiles showed an enrichment of several functions directly related to the biological and morphological characteristics of B-cells. In turn, about 30% of all protein-coding genes present in the whole human genome were identified as being expressed by the Ramos cells (stable average of 30% genes along all the chromosomes), revealing the size of the protein expression-set present in one specific human cell type. Additionally, the identification of missing proteins in our data sets has been reported, highlighting the power of the approach. Also, a comparison between neXtProt and UniProt database searches has been performed. In summary, our transcriptomic and proteomic experimental profiling provided a high coverage report of the expressed proteome from a human lymphoma B-cell type with a clear insight into the biological processes that characterized these cells. In this way, we demonstrated the usefulness of combining -omics for a comprehensive characterization of specific biological systems.

DTIStatistics facilitates and significantly automates the calculation of DTI group statistics by reducing the analysis times, which implies lower costs. DTIStatistics is highly applicable in clinical research, as demonstrated by the fact that it is currently being used at the University Hospital, University of Navarra (Spain).

This work presents an automatic method for distortion correction and calibration of intra-operative spine X-ray images, a fundamental step for the use of this modality in computer and robotic assisted surgeries. Our method is based on a prototype calibration drum, attached to the c-arm intensifier during the intervention. The projections of its embedded fiducial beads onto the X-ray images are segmented by the proposed method, which uses its calculated centroids to undo the distortion and, afterwards, calibrate the c-arm. For the latter purpose, we propose the use of a constrained version of the well known Direct Linear Transform (DLT) algorithm, reducing its degrees of freedom from 11 to 3. Experimental evaluation of our method is included in this work, showing that it is fast and more accurate than other existing methods. The low segmentation error level also ensures accurate calibration of the c-arm, with an expected error of 4% in the computation of its focal distance. (C) 2014 Elsevier Ltd. All rights reserved.

The integration of genomics and proteomics has led to the emergence of proteogenomics, a field of research successfully applied to the characterization of cancer samples. The diagnosis, prognosis and response to therapy of cancer patients will largely benefit from the identification of mutations present in their genome. The current state of the art of high throughput experiments for genome-wide detection of somatic mutations in cancer samples has allowed the development of projects such as the TCGA, in which hundreds of cancer genomes have been sequenced. This huge amount of data can be used to generate protein sequence databases in which each entry corresponds to a mutated peptide associated with certain cancer types. In this chapter, we describe a bioinformatics workflow for creating these databases and detecting mutated peptides in cancer samples from proteomic shotgun experiments. The performance of the proposed method has been evaluated using publicly available datasets from four cancer cell lines.